Abstract

A long term goal in user modeling for improving human-computer
interaction is to understand the user's intent based on her monitored
actions. We are developing an information retrieval system where the
task is to predict relevance for new documents, given judgments on old
ones. By monitoring the user's eye movements and inferring implicit
feedback from them we reduce the amount of tedious ranking of
retrieved documents, called relevance feedback in standard information
retrieval. Relevance is inferred with machine learning methods,
trained on eye movement patterns measured in settings where relevance
is known. Noise in the predictions is compensated for by fusing the
eye movements with other information about the user's preferences.
The goal is to make the information retrieval system proactive, that
is, capable of anticipating the user's interests.